Accuracy of State-level Surveillance During Emerging Outbreaks of Respiratory Viruses: A Model-based Assessment
27 Pages Posted: 8 May 2020
Date Written: April 23, 2020
It is long perceived that the more data collection, the more knowledge about the real disease progression. During emergencies like the H1N1 and the SARS-CoV-2 pandemics, public health surveillance requested increased testing capabilities to address the exacerbated demand. However, from the established viral surveillance, it is currently unknown how accurate it portrays disease progression through incidence and confirmed case trends. State viral surveillance – unlike commercial testing – can manage sampling and testing of specimens based on the upcoming demand (e.g., By restricting testing to high risk groups). Hence, proper assessment of accuracy may lead to improvements for a robust infrastructure.
Using the H1N1 pandemic experience, we developed a large-scale model that simulates the collection and testing of influenza specimens after an outbreak is declared in Michigan. We performed controlled experiments with simulation-based optimization to assess accuracy in terms of the biases between growth rates of original and observed influenza incidence trends.
We highlight the following results:
1) Emergency-driven high-risk perception increases reporting, which leads to reduction of biases in the growth rates,
2) The best predicted growth rates are those estimated from the trend of specimens submitted to the surveillance point that receives reports from a variety of healthcare providers, and
3) Under several criteria to queue specimens for viral subtyping with limited capacity, the best performing criterion was to queue first-come-first-serve restricted to specimens with higher hospitalization risk. Under this criterion, the lab released capacity to subtype specimens for each day in the trend, which reduced the growth rate bias the most compared to other queuing criteria. Future research should aim at investigating additional restrictions to the queue that further minimize the bias.
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